zarr_url = "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:sample-data/tutorial_data/cpm_v253/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.zarr"Accessing Zarr archives via GDAL in stars and terra
Introduction
This notebook demonstrates how to retrieve remotely stored Zarr data using the Zarr GDAL driver in R. We will explore how to read and visualize Zarr data (zarrays) and their metadata using the stars, sf and terra packages.
Note
This notebook has a sibling, which demonstrates how to access the same Zarr data using the Rarr package. You can find it here.
What we will learn
- ✏️ How to edit URLs of Zarr archives to make them readable for GDAL
- 🔎 Which read-functions and arguments to use in
starsandterra - 🚧 Current limitations of these packages
Prerequisites
We start at the point where you already have found the URL of your remote Zarr archive. This can be achived, e.g., using STAC, and is demonstrated in other notebooks in this series like “Access the EOPF Zarr STAC API with R”. Here we use the example STAC asset from the Sentinel-2 L1C MSI Zarr Product Exploration notebook which is hosted on EODC’s object storage.
Import packages
library(stars) # reading; imports sf too
library(terra) # reading
library(jsonlite) # parsing metadata
library(mapview) # interactive mapsGDAL’s Zarr driver
Common R-packages for raster data like stars and terra can leverage GDAL’s raster drivers to read data directly from remote locations. This is done using GDAL’s virtual file system (VSI) capabilities, specifically the /vsicurl/ prefix for accessing files over HTTP(S).
For Zarr data GDAL provides a dedicated Zarr driver that requires an additional ZARR:-prefix to the VSI URL. Finally, the complete prefix needs to be quoted: "ZARR:/vsicurl/".
vsi_prefix = "ZARR:/vsicurl/"
vsi_url = paste0(vsi_prefix, dplyr::as_label(zarr_url)) # needs special quoting
print(vsi_url)[1] "ZARR:/vsicurl/\"https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:sample-data/tutorial_data/cpm_v253/S2B_MSIL1C_20250113T103309_N0511_R108_T32TLQ_20250113T122458.zarr\""Metadata exploration with GDAL
With the VSI URL pointing to the remote Zarr archive we can now use GDAL utilities to explore the metadata of the Zarr archive. The sf::gdal_utils function provides an interface to GDAL command-line utilities. We use the mdiminfo command to retrieve metadata about the multidimensional arrays (zarrays) contained within the Zarr archive. We only print the top-level names of the arrays here.
sf::gdal_utils("mdiminfo", source = vsi_url, quiet = T) |>
fromJSON() |>
names()[1] "type" "driver" "name" "attributes" "groups" Click below for a full (and very long) output of mdiminfo:
sf::gdal_utils("mdiminfo", source = vsi_url){
"type": "group",
"driver": "Zarr",
"name": "/",
"attributes": {
"other_metadata": {
"L0_ancillary_data_quality": "null",
"L0_ephemeris_data_quality": "null",
"NUC_table_ID": 2,
"SWIR_rearrangement_flag": "null",
"UTM_zone_identification": "S2B_OPER_MSI_L1C_TL_2BPS_20250113T122458_A041032_T32TLQ_N05.11",
"absolute_location_assessment_from_AOCS": "null",
"band_description": {
"01": {
"bandwidth": "20.0",
"onboard_compression_rate": "2.655",
"onboard_integration_time": "7.22833",
"units": "nm"
},
"02": {
"bandwidth": "65.0",
"onboard_compression_rate": "2.97",
"onboard_integration_time": "1.2646269",
"units": "nm"
},
"03": {
"bandwidth": "35.0",
"onboard_compression_rate": "2.97",
"onboard_integration_time": "1.2996743",
"units": "nm"
},
"04": {
"bandwidth": "30.0",
"onboard_compression_rate": "2.97",
"onboard_integration_time": "1.3405629",
"units": "nm"
},
"05": {
"bandwidth": "15.0",
"onboard_compression_rate": "2.655",
"onboard_integration_time": "2.7846026",
"units": "nm"
},
"06": {
"bandwidth": "13.0",
"onboard_compression_rate": "2.655",
"onboard_integration_time": "2.7727115",
"units": "nm"
},
"07": {
"bandwidth": "19.0",
"onboard_compression_rate": "2.655",
"onboard_integration_time": "2.7727115",
"units": "nm"
},
"08": {
"bandwidth": "105.0",
"onboard_compression_rate": "2.97",
"onboard_integration_time": "1.3872929",
"units": "nm"
},
"09": {
"bandwidth": "20.0",
"onboard_compression_rate": "2.655",
"onboard_integration_time": "8.031501",
"units": "nm"
},
"10": {
"bandwidth": "30.0",
"onboard_compression_rate": "2.655",
"onboard_integration_time": "5.5537686",
"units": "nm"
},
"11": {
"bandwidth": "90.0",
"onboard_compression_rate": "2.4",
"onboard_integration_time": "1.3793689",
"units": "nm"
},
"12": {
"bandwidth": "180.0",
"onboard_compression_rate": "2.4",
"onboard_integration_time": "1.4761667",
"units": "nm"
},
"8A": {
"bandwidth": "20.0",
"onboard_compression_rate": "2.655",
"onboard_integration_time": "2.7608204",
"units": "nm"
},
"b01": {
"bandwidth": 20,
"central_wavelength": 442.30000000000001,
"onboard_compression_rate": "2.655",
"onboard_integration_time": "7.22833",
"physical_gain": "3.96033477",
"spectral_response_step": "1",
"spectral_response_values": "0.0062411 0.01024045 0.00402983 0.00642179 0.00552753 0.0065525 0.00409887 0.006297 0.00436742 0.00233356 0.00058162 0.00202276 0.00294328 0.00485362 0.00317041 0.00237657 0.00234612 0.00440152 0.01292397 0.05001678 0.18650104 0.45441623 0.72307877 0.83999211 0.86456334 0.87472096 0.89215296 0.91090814 0.92588017 0.93924094 0.94491826 0.95078529 0.96803023 0.99939195 1 0.97548364 0.96148351 0.94986211 0.91841452 0.87989802 0.80383677 0.59752075 0.30474132 0.10798014 0.0304465 0.00885119",
"units": "nm",
"wavelength_max": 456,
"wavelength_min": 411
},
"b02": {
"bandwidth": 65,
"central_wavelength": 492.30000000000001,
"onboard_compression_rate": "2.97",
"onboard_integration_time": "1.2646269",
"physical_gain": "3.81657264",
"spectral_response_step": "1",
"spectral_response_values": "0.05529541 0.12005068 0.25199051 0.4623617 0.65162379 0.77642171 0.82319091 0.83083116 0.83382106 0.837526 0.86304286 0.88226141 0.90486326 0.92043837 0.93602675 0.930533 0.92714067 0.9161479 0.90551724 0.89745515 0.90266694 0.90854264 0.92047913 0.92417935 0.91845025 0.90743244 0.89733983 0.88646415 0.87189983 0.85643973 0.84473414 0.84190734 0.85644111 0.87782724 0.90261174 0.91840544 0.94585847 0.96887192 0.99336135 0.99927899 1 0.99520325 0.98412711 0.97947473 0.97808297 0.97213439 0.96277794 0.95342234 0.93802376 0.92460144 0.90932642 0.90192251 0.89184298 0.88963556 0.89146958 0.89877911 0.91056869 0.92427362 0.93823555 0.95311791 0.97150808 0.98737003 0.99658514 0.99367959 0.98144714 0.95874415 0.89291635 0.73566218 0.52060373 0.3322804 0.19492197 0.11732617 0.07507304 0.05094154 0.03213016 0.01510217 0.00447984",
"units": "nm",
"wavelength_max": 532,
"wavelength_min": 456
},
"b03": {
"bandwidth": 35,
"central_wavelength": 559,
"onboard_compression_rate": "2.97",
"onboard_integration_time": "1.2996743",
"physical_gain": "4.23050273",
"spectral_response_step": "1",
"spectral_response_values": "0.00188039 0.01545903 0.03660414 0.08100583 0.16917887 0.33278274 0.58622794 0.80916412 0.913051 0.94472284 0.94898813 0.94369132 0.92845674 0.91256938 0.90078036 0.89958598 0.90547138 0.92045355 0.94065665 0.96199681 0.98186744 0.9985841 1 0.99279888 0.97801325 0.95301174 0.9266333 0.89359131 0.86941793 0.84827 0.83908301 0.83206209 0.8291787 0.83305844 0.84630939 0.86396307 0.87268076 0.86818339 0.8554947 0.80839054 0.67650876 0.45584205 0.24737576 0.12765465 0.0589016 0.02564742 0.00515905",
"units": "nm",
"wavelength_max": 582,
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},
"b04": {
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"units": "nm",
"wavelength_max": 685,
"wavelength_min": 646
},
"b05": {
"bandwidth": 15,
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"onboard_compression_rate": "2.655",
"onboard_integration_time": "2.7846026",
"physical_gain": "5.17726695",
"spectral_response_step": "1",
"spectral_response_values": "0.01042619 0.05713826 0.21461286 0.54715702 0.87088164 0.96808183 0.99104427 1 0.99512875 0.98751456 0.97910038 0.97035979 0.95875454 0.94130694 0.92531149 0.89283152 0.76531084 0.50228771 0.17957688 0.0337948 0.00240526",
"units": "nm",
"wavelength_max": 714,
"wavelength_min": 694
},
"b06": {
"bandwidth": 13,
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"onboard_compression_rate": "2.655",
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"physical_gain": "5.08823722",
"spectral_response_step": "1",
"spectral_response_values": "0.01739744 0.10565746 0.38571484 0.78168196 0.90518378 0.91562509 0.92258804 0.93134141 0.9469604 0.96535098 0.97817455 0.99107716 0.99990615 1 0.97144118 0.81937503 0.46748011 0.09409351 0.00983236",
"units": "nm",
"wavelength_max": 748,
"wavelength_min": 730
},
"b07": {
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"onboard_compression_rate": "2.655",
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"units": "nm",
"wavelength_max": 794,
"wavelength_min": 766
},
"b08": {
"bandwidth": 105,
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"onboard_compression_rate": "2.97",
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"physical_gain": "6.82374334",
"spectral_response_step": "1",
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"units": "nm",
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},
"b09": {
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},
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"units": "nm",
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},
"b11": {
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"spectral_response_values": "1.154e-05 2.707e-05 8.129e-05 0.0001701 0.00027422 0.00034456 0.00046028 0.00065214 0.00082283 0.00107791 0.0014306 0.00196134 0.00266427 0.00368682 0.00522456 0.00758401 0.01126335 0.01715812 0.02674581 0.04145595 0.06300627 0.09464207 0.13995799 0.20105412 0.28189591 0.38134665 0.4907345 0.60674263 0.71505301 0.80391496 0.87015099 0.91645643 0.94668952 0.96391534 0.97305962 0.97704089 0.97777566 0.97686717 0.97531356 0.97336816 0.9714563 0.9697157 0.96907419 0.96968255 0.97051178 0.97272986 0.97613656 0.97894419 0.9810083 0.98350836 0.9848292 0.98438948 0.98389859 0.98334634 0.9814301 0.97936035 0.97802641 0.97623515 0.97537114 0.97569131 0.97679261 0.97898052 0.98199689 0.98520852 0.98866135 0.99233425 0.99480248 0.99589079 0.9958911 0.99475534 0.99207775 0.98856394 0.9848769 0.98106836 0.97677436 0.97351815 0.97192459 0.97052192 0.97043004 0.9723835 0.97525347 0.97856769 0.98298866 0.98810437 0.99268138 0.99645012 0.9990686 1 0.99902738 0.99662493 0.99326995 0.989647 0.98577051 0.98212932 0.97979728 0.97946062 0.980262 0.98247241 0.98601349 0.98957829 0.99173488 0.99219848 0.98937107 0.98205611 0.97007817 0.95307506 0.93223131 0.90784439 0.88392149 0.86411672 0.85075738 0.84410342 0.84337963 0.84698191 0.84866039 0.84046041 0.81336359 0.75654857 0.66994259 0.56517119 0.45690882 0.35310835 0.25633426 0.17582806 0.11552613 0.0733101 0.04640345 0.02898639 0.01853597 0.01243537 0.00877131 0.00630418 0.00457459 0.00335323 0.00245906 0.001988 0.00149989 0.00112208 0.00078208 0.00054086 0.00028019 0.0001326",
"units": "nm",
"wavelength_max": 1679,
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},
"b12": {
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],
"used DEM file name": "S2__OPER_DEM_GLOBEF_PDMC_19800101T000000_S19800101T000000",
"used ECMWF file names": "S2__OPER_AUX_ECMWFD_ADG__20250113T000000_V20250113T090000_20250115T030000",
"used IERS file name": "S2__OPER_AUX_UT1UTC_PDMC_20250109T000000_V20250110T000000_20260109T000000"
},
"organisation": "ESA",
"output": "S2MSIL1A_etc",
"processor": "Sentinel-2 IPF",
"type": "Level-1A Product",
"version": "???"
},
{
"inputs": {
"Level-1A Product": "S2MSIL1A_etc",
"list of used processing parameters file names": [
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],
"used DEM file name": "S2__OPER_DEM_GLOBEF_PDMC_19800101T000000_S19800101T000000",
"used ECMWF file names": "S2__OPER_AUX_ECMWFD_ADG__20250113T000000_V20250113T090000_20250115T030000",
"used IERS file name": "S2__OPER_AUX_UT1UTC_PDMC_20250109T000000_V20250110T000000_20260109T000000"
},
"organisation": "ESA",
"output": "S2MSIL1B_etc",
"processor": "Sentinel-2 IPF",
"type": "Level-1B Product",
"version": "???"
},
{
"inputs": {
"Level-1B Product": "S2MSIL1B_etc",
"list of used processing parameters file names": [
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"/quality/mask/r20m/x"
],
"dimension_size": [
5490,
5490
],
"block_size": [
915,
915
],
"attributes": {
"_eopf_attrs": {
"coordinates": [
"x",
"y"
],
"dimensions": [
"y",
"x"
],
"flag_masks": [
1,
2,
4,
8,
16,
32,
64,
128
],
"flag_meanings": [
"ANC_LOST",
"ANC_DEG",
"MSI_LOST",
"MSI_DEG",
"QT_DEFECTIVE_PIXELS",
"QT_NODATA_PIXELS",
"QT_PARTIALLY_CORRECTED_PIXELS",
"QT_SATURATED_PIXELS_L1A"
]
},
"dtype": "<u1",
"flag_masks": [1, 2, 4, 8, 16, 32, 64, 128],
"flag_meanings": ["ANC_LOST", "ANC_DEG", "MSI_LOST", "MSI_DEG", "QT_DEFECTIVE_PIXELS", "QT_NODATA_PIXELS", "QT_PARTIALLY_CORRECTED_PIXELS", "QT_SATURATED_PIXELS_L1A"],
"long_name": "quality mask provided in the final reference frame (ground geometry)",
"proj:bbox": [300000, 4890240, 409800, 5000040],
"proj:epsg": 32632,
"proj:shape": [5490, 5490],
"proj:transform": [20, 0, 300000, 0, -20, 5000040, 0, 0, 1],
"proj:wkt2": "PROJCS[\"WGS 84 / UTM zone 32N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32632\"]]"
},
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
},
"b8a": {
"datatype": "Byte",
"dimensions": [
"/quality/mask/r20m/y",
"/quality/mask/r20m/x"
],
"dimension_size": [
5490,
5490
],
"block_size": [
915,
915
],
"attributes": {
"_eopf_attrs": {
"coordinates": [
"x",
"y"
],
"dimensions": [
"y",
"x"
],
"flag_masks": [
1,
2,
4,
8,
16,
32,
64,
128
],
"flag_meanings": [
"ANC_LOST",
"ANC_DEG",
"MSI_LOST",
"MSI_DEG",
"QT_DEFECTIVE_PIXELS",
"QT_NODATA_PIXELS",
"QT_PARTIALLY_CORRECTED_PIXELS",
"QT_SATURATED_PIXELS_L1A"
]
},
"dtype": "<u1",
"flag_masks": [1, 2, 4, 8, 16, 32, 64, 128],
"flag_meanings": ["ANC_LOST", "ANC_DEG", "MSI_LOST", "MSI_DEG", "QT_DEFECTIVE_PIXELS", "QT_NODATA_PIXELS", "QT_PARTIALLY_CORRECTED_PIXELS", "QT_SATURATED_PIXELS_L1A"],
"long_name": "quality mask provided in the final reference frame (ground geometry)",
"proj:bbox": [300000, 4890240, 409800, 5000040],
"proj:epsg": 32632,
"proj:shape": [5490, 5490],
"proj:transform": [20, 0, 300000, 0, -20, 5000040, 0, 0, 1],
"proj:wkt2": "PROJCS[\"WGS 84 / UTM zone 32N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32632\"]]"
},
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
}
}
},
"r60m": {
"dimensions": [
{
"name": "x",
"full_name": "/quality/mask/r60m/x",
"size": 1830,
"indexing_variable": {
"x": {
"datatype": "Int64",
"dimensions": [
"/quality/mask/r60m/x"
],
"dimension_size": [
1830
],
"block_size": [
1830
],
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
}
}
},
{
"name": "y",
"full_name": "/quality/mask/r60m/y",
"size": 1830,
"indexing_variable": {
"y": {
"datatype": "Int64",
"dimensions": [
"/quality/mask/r60m/y"
],
"dimension_size": [
1830
],
"block_size": [
1830
],
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
}
}
}
],
"arrays": {
"x": {
"datatype": "Int64",
"dimensions": [
"/quality/mask/r60m/x"
],
"dimension_size": [
1830
],
"block_size": [
1830
],
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
},
"y": {
"datatype": "Int64",
"dimensions": [
"/quality/mask/r60m/y"
],
"dimension_size": [
1830
],
"block_size": [
1830
],
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
},
"b01": {
"datatype": "Byte",
"dimensions": [
"/quality/mask/r60m/y",
"/quality/mask/r60m/x"
],
"dimension_size": [
1830,
1830
],
"block_size": [
305,
305
],
"attributes": {
"_eopf_attrs": {
"coordinates": [
"x",
"y"
],
"dimensions": [
"y",
"x"
],
"flag_masks": [
1,
2,
4,
8,
16,
32,
64,
128
],
"flag_meanings": [
"ANC_LOST",
"ANC_DEG",
"MSI_LOST",
"MSI_DEG",
"QT_DEFECTIVE_PIXELS",
"QT_NODATA_PIXELS",
"QT_PARTIALLY_CORRECTED_PIXELS",
"QT_SATURATED_PIXELS_L1A"
]
},
"dtype": "<u1",
"flag_masks": [1, 2, 4, 8, 16, 32, 64, 128],
"flag_meanings": ["ANC_LOST", "ANC_DEG", "MSI_LOST", "MSI_DEG", "QT_DEFECTIVE_PIXELS", "QT_NODATA_PIXELS", "QT_PARTIALLY_CORRECTED_PIXELS", "QT_SATURATED_PIXELS_L1A"],
"long_name": "quality mask provided in the final reference frame (ground geometry)",
"proj:bbox": [300000, 4890240, 409800, 5000040],
"proj:epsg": 32632,
"proj:shape": [1830, 1830],
"proj:transform": [60, 0, 300000, 0, -60, 5000040, 0, 0, 1],
"proj:wkt2": "PROJCS[\"WGS 84 / UTM zone 32N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32632\"]]"
},
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
},
"b09": {
"datatype": "Byte",
"dimensions": [
"/quality/mask/r60m/y",
"/quality/mask/r60m/x"
],
"dimension_size": [
1830,
1830
],
"block_size": [
305,
305
],
"attributes": {
"_eopf_attrs": {
"coordinates": [
"x",
"y"
],
"dimensions": [
"y",
"x"
],
"flag_masks": [
1,
2,
4,
8,
16,
32,
64,
128
],
"flag_meanings": [
"ANC_LOST",
"ANC_DEG",
"MSI_LOST",
"MSI_DEG",
"QT_DEFECTIVE_PIXELS",
"QT_NODATA_PIXELS",
"QT_PARTIALLY_CORRECTED_PIXELS",
"QT_SATURATED_PIXELS_L1A"
]
},
"dtype": "<u1",
"flag_masks": [1, 2, 4, 8, 16, 32, 64, 128],
"flag_meanings": ["ANC_LOST", "ANC_DEG", "MSI_LOST", "MSI_DEG", "QT_DEFECTIVE_PIXELS", "QT_NODATA_PIXELS", "QT_PARTIALLY_CORRECTED_PIXELS", "QT_SATURATED_PIXELS_L1A"],
"long_name": "quality mask provided in the final reference frame (ground geometry)",
"proj:bbox": [300000, 4890240, 409800, 5000040],
"proj:epsg": 32632,
"proj:shape": [1830, 1830],
"proj:transform": [60, 0, 300000, 0, -60, 5000040, 0, 0, 1],
"proj:wkt2": "PROJCS[\"WGS 84 / UTM zone 32N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32632\"]]"
},
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
},
"b10": {
"datatype": "Byte",
"dimensions": [
"/quality/mask/r60m/y",
"/quality/mask/r60m/x"
],
"dimension_size": [
1830,
1830
],
"block_size": [
305,
305
],
"attributes": {
"_eopf_attrs": {
"coordinates": [
"x",
"y"
],
"dimensions": [
"y",
"x"
],
"flag_masks": [
1,
2,
4,
8,
16,
32,
64,
128
],
"flag_meanings": [
"ANC_LOST",
"ANC_DEG",
"MSI_LOST",
"MSI_DEG",
"QT_DEFECTIVE_PIXELS",
"QT_NODATA_PIXELS",
"QT_PARTIALLY_CORRECTED_PIXELS",
"QT_SATURATED_PIXELS_L1A"
]
},
"dtype": "<u1",
"flag_masks": [1, 2, 4, 8, 16, 32, 64, 128],
"flag_meanings": ["ANC_LOST", "ANC_DEG", "MSI_LOST", "MSI_DEG", "QT_DEFECTIVE_PIXELS", "QT_NODATA_PIXELS", "QT_PARTIALLY_CORRECTED_PIXELS", "QT_SATURATED_PIXELS_L1A"],
"long_name": "quality mask provided in the final reference frame (ground geometry)",
"proj:bbox": [300000, 4890240, 409800, 5000040],
"proj:epsg": 32632,
"proj:shape": [1830, 1830],
"proj:transform": [60, 0, 300000, 0, -60, 5000040, 0, 0, 1],
"proj:wkt2": "PROJCS[\"WGS 84 / UTM zone 32N\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",9],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"32632\"]]"
},
"structural_info": {
"COMPRESSOR": "{ \"blocksize\": 0, \"clevel\": 3, \"cname\": \"zstd\", \"id\": \"blosc\", \"shuffle\": 2 }"
}
}
}
}
}
}
}
}
}
}Reading Zarr data with stars and terra
Note
In this section we show the current capabilities and limitations of stars and terra when it comes to reading Zarr array(s). Successful attempts are marked with a ✅, failed attempts with a ⛔.
First we define the path to a specific data array (band 1, 60 meter resolution) inside the Zarr archive which can be understood as a single raster layer.
band_variable = "/measurements/reflectance/r60m/b01"stars::read_stars()
⛔ Traditional approach for reading all bands/layers and specifying a driver.
r = read_stars(vsi_url, driver = "ZARR")NA, Error in if (is.function(.x) || !np || any(sapply(prefixes, has_prefix, : missing value where TRUE/FALSE needed⛔ Traditional approach for reading all bands/layers and specifying a sub-dataset (integer).
r = read_stars(vsi_url, sub = 1)NA, Error in if (is.function(.x) || !np || any(sapply(prefixes, has_prefix, : missing value where TRUE/FALSE needed⛔ Traditional approach for reading all bands/layers and specifying a sub-dataset (path).
r = read_stars(vsi_url, sub = band_variable)Error in strsplit(x, ":"): non-character argument✅ Constructing the full Zarray path from prefix, URL, and band variable.
(r = read_stars(paste(vsi_url, band_variable, sep = ":")))Warning: ignoring unrecognized unit: digital_countsstars object with 2 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max.
b01 0.1299 0.1542 0.1628 0.3541124 0.4304 1.6556
dimension(s):
from to offset delta x/y
x 1 1830 3e+05 60 [x]
y 1 1830 5e+06 -60 [y]st_crs(r) # NA: emptyCoordinate Reference System: NAst_crs(r) = 'EPSG:32632'
system.time(plot(r, axes = TRUE)) 
user system elapsed
4.615 0.303 4.905 This method successfully reads the specified band from the remote Zarr archive.
🚧 However, note that the coordinate reference system (CRS) is not automatically recognized and needs to be set manually.
Interactive visualization:
mapview(r)Number of pixels is above 5e+05. Automatic downsampling of `stars` object is not yet implemented, so rendering may be slow.
You can pass a `stars` proxy object to mapview to get automatic downsampling.stars::read_mdim()
⛔ Traditional approach for reading all bands/layers at once. “?” should return a list of array possible names.
m = read_mdim(vsi_url)Error: no array names foundm = read_mdim(vsi_url, variable = "?") # query array namesError in d[[rd[1]]]: attempt to select less than one element in get1index✅ Specifying the full Zarray path from prefix, URL, and band variable. Read as proxy object.
system.time({
(m = read_mdim(vsi_url, variable = band_variable, proxy = TRUE))
}) # fast: only reads metadata user system elapsed
0.206 0.009 0.340 st_crs(m) # NA: emptyCoordinate Reference System: NAsystem.time(plot(m, axes = T)) # slow: only here the full array is downloadedWarning: ignoring unrecognized unit: digital_counts
user system elapsed
7.101 0.138 12.496 As for read_stars(), the CRS is not automatically recognized and needs to be set manually.
terra::rast()
✅ / ⛔ Traditional approach for reading all bands/layers at once.
(tr = terra::rast(vsi_url))class : SpatRaster
size : 10980, 10980, 15 (nrow, ncol, nlyr)
resolution : 10, 10 (x, y)
extent : 3e+05, 409800, 4890240, 5000040 (xmin, xmax, ymin, ymax)
coord. ref. :
sources : b02
b03
b04
... and 10 more sources
names : b02, b03, b04, b08, b02, b03, ... tr$b02Error: [subset] you cannot select a layer with a name that is not uniquenames(tr) # has non-unique names, e.g. three times "b02" [1] "b02" "b03" "b04" "b08" "b02" "b03" "b04" "b08" "tci_1"
[10] "tci_2" "tci_3" "b02" "b03" "b04" "b08" terra reads some of the available bands, but fails to name them uniquely. It likely searches for band names like “b02” and finds the first group of data arrays by order, in this case the 10-meter bands (2, 3, 4, and 8). The Zarr archive stores multiple array named by band such as “/measurements/reflectance/r10m/b02”, their corresponding masks (e.g. “quality/mask/r10m/b02) and the detector footprint (e.g. ”/conditions/mask/detector_footprint/r10m/b02”). All Zarrays share the same name, “b02”, leading to non-unique layer names in the resulting SpatRaster object. When constructing unique names we can at least visualize the data.
names(tr) = paste0(letters[1:nlyr(tr)], names(tr)) # rename layers to unique names
names(tr) [1] "ab02" "bb03" "cb04" "db08" "eb02" "fb03" "gb04" "hb08"
[9] "itci_1" "jtci_2" "ktci_3" "lb02" "mb03" "nb04" "ob08" plot(tr)
✅ Specifying the full Zarray path from prefix, URL, and band variable as sub-dataset. Read as proxy object.
system.time({
tr = rast(vsi_url, subds = band_variable)
})# fast: only reads metadata user system elapsed
0.668 0.002 0.793 crs(tr) # "": empty[1] ""system.time(plot(tr)) # slow: only here the full array is downloaded 
user system elapsed
6.997 0.141 12.424 As for the stars-functions, the CRS is not automatically recognized and needs to be set manually.
💪 Now it is your turn
- 🔍 Task: Explore the metadata of other Zarr archives using GDAL’s
mdiminfocommand viasf::gdal_utils(). Store the JSON output in an R-object and find entries that store relevant metadata, such as the CRS. - Read the GDAL Zarr driver documentation to learn more about opening options.
Conclusion
In this notebook we have demonstrated how to access remote Zarr archives using GDAL’s Zarr driver in R with the stars and terra packages. We have seen that while it is possible to read specific data arrays, there are some limitations, such as the lack of automatic CRS recognition and challenges with reading multiple arrays simultaneously.
What’s next?
Explore this related notebook which demonstrates how to access the same Zarr data using the Rarr package and building a stars object from it.